Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations152
Missing cells454
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.0 KiB
Average record size in memory208.9 B

Variable types

Numeric20
Categorical2
DateTime4

Alerts

centr_2_turb_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
dur_cf is highly overall correlated with orden_encadenado_cfHigh correlation
dur_pino is highly overall correlated with lote and 1 other fieldsHigh correlation
id_centr is highly overall correlated with loteHigh correlation
lote is highly overall correlated with dur_pino and 3 other fieldsHigh correlation
lote_parental_cf is highly overall correlated with lote and 3 other fieldsHigh correlation
orden_encadenado_cf is highly overall correlated with dur_cf and 9 other fieldsHigh correlation
ph_1 is highly overall correlated with orden_encadenado_cf and 2 other fieldsHigh correlation
ph_2 is highly overall correlated with orden_encadenado_cf and 3 other fieldsHigh correlation
ph_pino is highly overall correlated with orden_encadenado_cf and 2 other fieldsHigh correlation
producto_1_cf is highly overall correlated with producto_2_cfHigh correlation
producto_2_cf is highly overall correlated with producto_1_cfHigh correlation
turb_1 is highly overall correlated with orden_encadenado_cf and 2 other fieldsHigh correlation
turb_2 is highly overall correlated with orden_encadenado_cf and 3 other fieldsHigh correlation
turb_fin_cultivo_cf is highly overall correlated with centr_2_turb_cf and 1 other fieldsHigh correlation
turb_inicio_cultivo_cf is highly overall correlated with lote_parental_cfHigh correlation
turb_pino is highly overall correlated with orden_encadenado_cf and 2 other fieldsHigh correlation
turbidez_diff_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
vol_ino_util_cf is highly overall correlated with lote_parental_cfHigh correlation
orden_encadenado_cf is highly imbalanced (55.1%)Imbalance
lote_parental_cf has 130 (85.5%) missing valuesMissing
vol_ino_util_cf has 5 (3.3%) missing valuesMissing
centr_1_turb_cf has 4 (2.6%) missing valuesMissing
centr_2_turb_cf has 9 (5.9%) missing valuesMissing
ph_pino has 34 (22.4%) missing valuesMissing
turb_pino has 34 (22.4%) missing valuesMissing
f_h_inicio_pino has 34 (22.4%) missing valuesMissing
f_h_fin_pino has 34 (22.4%) missing valuesMissing
dur_pino has 34 (22.4%) missing valuesMissing
ph_1 has 34 (22.4%) missing valuesMissing
turb_1 has 34 (22.4%) missing valuesMissing
ph_2 has 34 (22.4%) missing valuesMissing
turb_2 has 34 (22.4%) missing valuesMissing
lote has unique valuesUnique

Reproduction

Analysis started2024-10-13 15:47:00.659639
Analysis finished2024-10-13 15:47:29.661389
Duration29 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

lote
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct152
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23323.151
Minimum23019
Maximum24053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:29.709002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23019
5-th percentile23026.55
Q123060.75
median23101.5
Q324003.25
95-th percentile24044.45
Maximum24053
Range1034
Interquartile range (IQR)942.5

Descriptive statistics

Standard deviation416.71493
Coefficient of variation (CV)0.017867008
Kurtosis-0.75084625
Mean23323.151
Median Absolute Deviation (MAD)43
Skewness1.1084266
Sum3545119
Variance173651.33
MonotonicityNot monotonic
2024-10-13T17:47:29.780088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23019 1
 
0.7%
23131 1
 
0.7%
23123 1
 
0.7%
23124 1
 
0.7%
23125 1
 
0.7%
23126 1
 
0.7%
23127 1
 
0.7%
23129 1
 
0.7%
23130 1
 
0.7%
23132 1
 
0.7%
Other values (142) 142
93.4%
ValueCountFrequency (%)
23019 1
0.7%
23020 1
0.7%
23021 1
0.7%
23022 1
0.7%
23023 1
0.7%
23024 1
0.7%
23025 1
0.7%
23026 1
0.7%
23027 1
0.7%
23028 1
0.7%
ValueCountFrequency (%)
24053 1
0.7%
24052 1
0.7%
24051 1
0.7%
24050 1
0.7%
24049 1
0.7%
24047 1
0.7%
24046 1
0.7%
24045 1
0.7%
24044 1
0.7%
24043 1
0.7%

orden_encadenado_cf
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
127 
2
23 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters152
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Length

2024-10-13T17:47:29.854159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:47:29.906517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

lote_parental_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing130
Missing (%)85.5%
Infinite0
Infinite (%)0.0%
Mean23571.818
Minimum23085
Maximum24051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:29.960978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23085
5-th percentile23099.05
Q123112.25
median23567.5
Q324034.75
95-th percentile24049.7
Maximum24051
Range966
Interquartile range (IQR)922.5

Descriptive statistics

Standard deviation472.6577
Coefficient of variation (CV)0.020051813
Kurtosis-2.2071206
Mean23571.818
Median Absolute Deviation (MAD)459.5
Skewness0.00017902579
Sum518580
Variance223405.3
MonotonicityNot monotonic
2024-10-13T17:47:30.021126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24010 1
 
0.7%
24050 1
 
0.7%
24044 1
 
0.7%
24041 1
 
0.7%
24036 1
 
0.7%
24037 1
 
0.7%
24031 1
 
0.7%
24027 1
 
0.7%
24021 1
 
0.7%
24020 1
 
0.7%
Other values (12) 12
 
7.9%
(Missing) 130
85.5%
ValueCountFrequency (%)
23085 1
0.7%
23099 1
0.7%
23100 1
0.7%
23108 1
0.7%
23109 1
0.7%
23112 1
0.7%
23113 1
0.7%
23118 1
0.7%
23119 1
0.7%
23124 1
0.7%
ValueCountFrequency (%)
24051 1
0.7%
24050 1
0.7%
24044 1
0.7%
24041 1
0.7%
24037 1
0.7%
24036 1
0.7%
24031 1
0.7%
24027 1
0.7%
24021 1
0.7%
24020 1
0.7%

id_bio
Real number (ℝ)

Distinct7
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14120.803
Minimum13169
Maximum14617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:30.073415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13169
5-th percentile13169
Q113170
median14614
Q314616
95-th percentile14617
Maximum14617
Range1448
Interquartile range (IQR)1446

Descriptive statistics

Standard deviation687.94411
Coefficient of variation (CV)0.048718485
Kurtosis-1.5687935
Mean14120.803
Median Absolute Deviation (MAD)2
Skewness-0.67230589
Sum2146362
Variance473267.1
MonotonicityNot monotonic
2024-10-13T17:47:30.126499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14616 34
22.4%
14615 30
19.7%
13170 29
19.1%
14614 27
17.8%
13169 22
14.5%
14617 9
 
5.9%
13189 1
 
0.7%
ValueCountFrequency (%)
13169 22
14.5%
13170 29
19.1%
13189 1
 
0.7%
14614 27
17.8%
14615 30
19.7%
14616 34
22.4%
14617 9
 
5.9%
ValueCountFrequency (%)
14617 9
 
5.9%
14616 34
22.4%
14615 30
19.7%
14614 27
17.8%
13189 1
 
0.7%
13170 29
19.1%
13169 22
14.5%
Distinct103
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-21 06:30:00+00:00
Maximum2024-03-25 12:28:00+00:00
2024-10-13T17:47:30.193273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:30.267797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct137
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-23 05:30:00+00:00
Maximum2024-03-27 07:51:00+00:00
2024-10-13T17:47:30.345713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:30.420796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vol_ino_util_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)19.7%
Missing5
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean81.458503
Minimum66.4
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:30.491194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum66.4
5-th percentile79.44
Q180
median81.6
Q382.8
95-th percentile84.16
Maximum88
Range21.6
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.248108
Coefficient of variation (CV)0.027598199
Kurtosis13.302453
Mean81.458503
Median Absolute Deviation (MAD)1.6
Skewness-1.7398503
Sum11974.4
Variance5.0539895
MonotonicityNot monotonic
2024-10-13T17:47:30.549721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
80 44
28.9%
82.4 12
 
7.9%
83.2 12
 
7.9%
81.6 11
 
7.2%
82 11
 
7.2%
80.8 7
 
4.6%
84 7
 
4.6%
83.6 6
 
3.9%
81.2 6
 
3.9%
80.4 5
 
3.3%
Other values (19) 26
17.1%
ValueCountFrequency (%)
66.4 1
 
0.7%
76 1
 
0.7%
77.2 1
 
0.7%
77.6 2
 
1.3%
78.4 1
 
0.7%
78.8 1
 
0.7%
79.2 1
 
0.7%
80 44
28.9%
80.4 5
 
3.3%
80.56 1
 
0.7%
ValueCountFrequency (%)
88 1
 
0.7%
87.2 1
 
0.7%
86.4 1
 
0.7%
85.92 1
 
0.7%
85.6 1
 
0.7%
85.2 1
 
0.7%
84.16 3
 
2.0%
84 7
4.6%
83.6 6
3.9%
83.2 12
7.9%

turb_inicio_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.036316
Minimum12.56
Maximum44.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:30.610788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.56
5-th percentile14.8
Q116.4
median17.76
Q318.8
95-th percentile21.796
Maximum44.4
Range31.84
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation3.3008587
Coefficient of variation (CV)0.18301181
Kurtosis28.581161
Mean18.036316
Median Absolute Deviation (MAD)1.16
Skewness4.248607
Sum2741.52
Variance10.895668
MonotonicityNot monotonic
2024-10-13T17:47:30.680830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.84 6
 
3.9%
16.16 5
 
3.3%
16.64 5
 
3.3%
18.32 5
 
3.3%
17.76 5
 
3.3%
18 4
 
2.6%
18.72 4
 
2.6%
15.28 4
 
2.6%
17.6 4
 
2.6%
17.12 4
 
2.6%
Other values (62) 106
69.7%
ValueCountFrequency (%)
12.56 1
0.7%
13.36 1
0.7%
14.08 1
0.7%
14.4 1
0.7%
14.48 1
0.7%
14.56 2
1.3%
14.8 2
1.3%
14.88 2
1.3%
14.96 1
0.7%
15.04 1
0.7%
ValueCountFrequency (%)
44.4 1
0.7%
30.32 2
1.3%
27.04 1
0.7%
26.24 1
0.7%
23.2 1
0.7%
22 1
0.7%
21.84 1
0.7%
21.76 1
0.7%
21.44 1
0.7%
20.8 2
1.3%

turb_fin_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.416316
Minimum42.8
Maximum91.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:30.873331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42.8
5-th percentile59.828
Q169.1
median74.32
Q381.08
95-th percentile87.2
Maximum91.2
Range48.4
Interquartile range (IQR)11.98

Descriptive statistics

Standard deviation8.9408989
Coefficient of variation (CV)0.12014702
Kurtosis1.2364086
Mean74.416316
Median Absolute Deviation (MAD)5.64
Skewness-0.75227869
Sum11311.28
Variance79.939674
MonotonicityNot monotonic
2024-10-13T17:47:30.948124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 7
 
4.6%
83.2 6
 
3.9%
81.6 5
 
3.3%
80.8 4
 
2.6%
85.6 4
 
2.6%
74.4 3
 
2.0%
87.2 3
 
2.0%
69.04 3
 
2.0%
72.48 3
 
2.0%
73.52 2
 
1.3%
Other values (96) 112
73.7%
ValueCountFrequency (%)
42.8 1
0.7%
44.32 1
0.7%
49.36 1
0.7%
49.76 1
0.7%
54.16 1
0.7%
56.48 1
0.7%
56.96 1
0.7%
59.52 1
0.7%
60.08 1
0.7%
60.72 1
0.7%
ValueCountFrequency (%)
91.2 2
 
1.3%
90.4 2
 
1.3%
89.6 1
 
0.7%
88 1
 
0.7%
87.2 3
2.0%
86.4 2
 
1.3%
85.6 4
2.6%
84.8 2
 
1.3%
84 7
4.6%
83.2 6
3.9%

viab_final_cultivo_cf
Real number (ℝ)

Distinct101
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016579 × 108
Minimum70400000
Maximum3.696 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.014670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum70400000
5-th percentile1.1448 × 108
Q11.48 × 108
median1.652 × 108
Q31.922 × 108
95-th percentile2.2828 × 108
Maximum3.696 × 108
Range2.992 × 108
Interquartile range (IQR)44200000

Descriptive statistics

Standard deviation38308279
Coefficient of variation (CV)0.22512327
Kurtosis4.5153204
Mean1.7016579 × 108
Median Absolute Deviation (MAD)20400000
Skewness0.99890815
Sum2.58652 × 1010
Variance1.4675242 × 1015
MonotonicityNot monotonic
2024-10-13T17:47:31.087171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195200000 5
 
3.3%
164000000 4
 
2.6%
145600000 4
 
2.6%
185600000 3
 
2.0%
157600000 3
 
2.0%
158400000 3
 
2.0%
163200000 3
 
2.0%
153600000 3
 
2.0%
156800000 3
 
2.0%
184000000 3
 
2.0%
Other values (91) 118
77.6%
ValueCountFrequency (%)
70400000 1
0.7%
91200000 1
0.7%
95200000 1
0.7%
97600000 1
0.7%
100000000 1
0.7%
101600000 1
0.7%
104000000 1
0.7%
113600000 1
0.7%
115200000 1
0.7%
117600000 1
0.7%
ValueCountFrequency (%)
369600000 1
0.7%
280000000 1
0.7%
262400000 1
0.7%
260000000 1
0.7%
248000000 1
0.7%
240000000 1
0.7%
232000000 1
0.7%
229600000 1
0.7%
227200000 1
0.7%
224000000 1
0.7%

id_centr
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
14246
60 
17825
54 
12912
36 
6379
 
2

Length

Max length5
Median length5
Mean length4.9868421
Min length4

Characters and Unicode

Total characters758
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17825
2nd row14246
3rd row17825
4th row12912
5th row17825

Common Values

ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Length

2024-10-13T17:47:31.158638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:47:31.214494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

centr_1_turb_cf
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)62.2%
Missing4
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean30.067703
Minimum21.28
Maximum168.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.278759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21.28
5-th percentile23.096
Q126.44
median28.56
Q330.5
95-th percentile33.44
Maximum168.8
Range147.52
Interquartile range (IQR)4.06

Descriptive statistics

Standard deviation15.167552
Coefficient of variation (CV)0.50444664
Kurtosis67.864839
Mean30.067703
Median Absolute Deviation (MAD)2.04
Skewness8.063087
Sum4450.02
Variance230.05462
MonotonicityNot monotonic
2024-10-13T17:47:31.350192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.84 7
 
4.6%
28.72 5
 
3.3%
29.44 4
 
2.6%
28.56 4
 
2.6%
30.4 4
 
2.6%
26.56 4
 
2.6%
27.44 3
 
2.0%
30.16 3
 
2.0%
31.76 3
 
2.0%
29.52 3
 
2.0%
Other values (82) 108
71.1%
(Missing) 4
 
2.6%
ValueCountFrequency (%)
21.28 1
0.7%
21.52 1
0.7%
21.76 1
0.7%
21.84 1
0.7%
22.08 1
0.7%
22.4 1
0.7%
22.64 1
0.7%
23.04 1
0.7%
23.2 1
0.7%
23.28 1
0.7%
ValueCountFrequency (%)
168.8 1
 
0.7%
142.4 1
 
0.7%
40.9 1
 
0.7%
36.64 1
 
0.7%
34.48 1
 
0.7%
34 1
 
0.7%
33.6 1
 
0.7%
33.44 3
2.0%
33.2 1
 
0.7%
32.72 1
 
0.7%

centr_2_turb_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)78.3%
Missing9
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean23.56979
Minimum9.84
Maximum156.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.421088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.84
5-th percentile12.2
Q117.72
median20.72
Q325
95-th percentile37.704
Maximum156.96
Range147.12
Interquartile range (IQR)7.28

Descriptive statistics

Standard deviation17.21646
Coefficient of variation (CV)0.73044604
Kurtosis46.177047
Mean23.56979
Median Absolute Deviation (MAD)3.84
Skewness6.3247727
Sum3370.48
Variance296.4065
MonotonicityNot monotonic
2024-10-13T17:47:31.491555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
2.6%
21.36 4
 
2.6%
20.8 4
 
2.6%
17.76 3
 
2.0%
19.52 3
 
2.0%
20.88 2
 
1.3%
17.84 2
 
1.3%
22.24 2
 
1.3%
15.36 2
 
1.3%
20.72 2
 
1.3%
Other values (102) 115
75.7%
(Missing) 9
 
5.9%
ValueCountFrequency (%)
9.84 1
0.7%
10.08 1
0.7%
10.4 2
1.3%
11.44 1
0.7%
11.6 2
1.3%
12.16 1
0.7%
12.56 1
0.7%
12.88 1
0.7%
13.2 1
0.7%
13.36 1
0.7%
ValueCountFrequency (%)
156.96 1
0.7%
151.76 1
0.7%
54.8 1
0.7%
49.04 1
0.7%
44.4 1
0.7%
44 1
0.7%
38.4 1
0.7%
37.84 1
0.7%
36.48 1
0.7%
34.48 1
0.7%

producto_1_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.3157
Minimum526.4
Maximum2395.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.562084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum526.4
5-th percentile1175.112
Q11466.76
median1675.4
Q31853.798
95-th percentile2140.896
Maximum2395.36
Range1868.96
Interquartile range (IQR)387.038

Descriptive statistics

Standard deviation307.71306
Coefficient of variation (CV)0.18555758
Kurtosis0.45810105
Mean1658.3157
Median Absolute Deviation (MAD)197.08
Skewness-0.34206353
Sum252063.99
Variance94687.327
MonotonicityNot monotonic
2024-10-13T17:47:31.635895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1468.88 2
 
1.3%
1517.92 2
 
1.3%
1747.92 1
 
0.7%
1902.96 1
 
0.7%
1978.16 1
 
0.7%
2117.76 1
 
0.7%
1688.08 1
 
0.7%
2395.36 1
 
0.7%
2155.76 1
 
0.7%
1116.64 1
 
0.7%
Other values (140) 140
92.1%
ValueCountFrequency (%)
526.4 1
0.7%
969.888 1
0.7%
970.8 1
0.7%
988.096 1
0.7%
1096.584 1
0.7%
1101.04 1
0.7%
1116.64 1
0.7%
1151.44 1
0.7%
1194.48 1
0.7%
1198.16 1
0.7%
ValueCountFrequency (%)
2395.36 1
0.7%
2338.56 1
0.7%
2263.2 1
0.7%
2162.48 1
0.7%
2161.12 1
0.7%
2155.76 1
0.7%
2151.536 1
0.7%
2150.576 1
0.7%
2132.976 1
0.7%
2129.92 1
0.7%

producto_2_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1209789
Minimum2.8
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.707872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.936
Q15.1
median6.08
Q37.12
95-th percentile8.312
Maximum9.2
Range6.4
Interquartile range (IQR)2.02

Descriptive statistics

Standard deviation1.4079732
Coefficient of variation (CV)0.23002418
Kurtosis-0.57667228
Mean6.1209789
Median Absolute Deviation (MAD)1.04
Skewness-0.0097291881
Sum930.3888
Variance1.9823884
MonotonicityNot monotonic
2024-10-13T17:47:31.775665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.16 6
 
3.9%
6.88 5
 
3.3%
6.56 5
 
3.3%
5.44 5
 
3.3%
5.52 5
 
3.3%
4.48 5
 
3.3%
5.76 4
 
2.6%
6.72 4
 
2.6%
5.28 4
 
2.6%
4.88 4
 
2.6%
Other values (54) 105
69.1%
ValueCountFrequency (%)
2.8 1
 
0.7%
2.96 1
 
0.7%
3.04 1
 
0.7%
3.44 2
1.3%
3.6 1
 
0.7%
3.68 1
 
0.7%
3.76 1
 
0.7%
4.08 2
1.3%
4.16 2
1.3%
4.24 3
2.0%
ValueCountFrequency (%)
9.2 1
 
0.7%
9.12 1
 
0.7%
8.96 1
 
0.7%
8.72 2
1.3%
8.64 1
 
0.7%
8.48 1
 
0.7%
8.4 1
 
0.7%
8.24 1
 
0.7%
8.16 2
1.3%
8.08 4
2.6%

dur_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173236.18
Minimum151200
Maximum193500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.846898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum151200
5-th percentile159600
Q1171045
median174150
Q3177300
95-th percentile182700
Maximum193500
Range42300
Interquartile range (IQR)6255

Descriptive statistics

Standard deviation7021.1053
Coefficient of variation (CV)0.040529092
Kurtosis0.60961578
Mean173236.18
Median Absolute Deviation (MAD)3150
Skewness-0.53540339
Sum26331900
Variance49295919
MonotonicityNot monotonic
2024-10-13T17:47:31.918629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172800 15
 
9.9%
176400 11
 
7.2%
178200 9
 
5.9%
174600 7
 
4.6%
169200 6
 
3.9%
171900 5
 
3.3%
180900 4
 
2.6%
171300 4
 
2.6%
175200 4
 
2.6%
162000 4
 
2.6%
Other values (57) 83
54.6%
ValueCountFrequency (%)
151200 1
 
0.7%
156180 1
 
0.7%
157200 1
 
0.7%
157500 1
 
0.7%
158400 2
1.3%
159300 1
 
0.7%
159600 3
2.0%
161100 2
1.3%
162000 4
2.6%
162300 1
 
0.7%
ValueCountFrequency (%)
193500 1
 
0.7%
189000 1
 
0.7%
187200 1
 
0.7%
185700 1
 
0.7%
185400 1
 
0.7%
183300 1
 
0.7%
182700 3
2.0%
181980 1
 
0.7%
181800 3
2.0%
181500 1
 
0.7%

turbidez_diff_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.38
Minimum24.72
Maximum73.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:31.986863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.72
5-th percentile41.688
Q151.04
median56.32
Q363.6
95-th percentile69.592
Maximum73.92
Range49.2
Interquartile range (IQR)12.56

Descriptive statistics

Standard deviation9.1640079
Coefficient of variation (CV)0.16254005
Kurtosis0.77084379
Mean56.38
Median Absolute Deviation (MAD)6.6
Skewness-0.62096502
Sum8569.76
Variance83.979041
MonotonicityNot monotonic
2024-10-13T17:47:32.056091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.48 3
 
2.0%
49.52 3
 
2.0%
65.2 2
 
1.3%
49.36 2
 
1.3%
48.08 2
 
1.3%
52.8 2
 
1.3%
68.16 2
 
1.3%
44.72 2
 
1.3%
54.16 2
 
1.3%
62.96 2
 
1.3%
Other values (128) 130
85.5%
ValueCountFrequency (%)
24.72 1
0.7%
29.84 1
0.7%
30.96 1
0.7%
31.12 1
0.7%
34.96 1
0.7%
37.2 1
0.7%
40.8 1
0.7%
41.6 1
0.7%
41.76 1
0.7%
42.4 1
0.7%
ValueCountFrequency (%)
73.92 1
0.7%
73.28 1
0.7%
72.48 1
0.7%
72.4 2
1.3%
70.64 1
0.7%
70.24 1
0.7%
69.68 1
0.7%
69.52 1
0.7%
69.44 1
0.7%
69.12 1
0.7%

ph_pino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)28.0%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean5.4522034
Minimum5.232
Maximum5.624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:32.119602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.232
5-th percentile5.3188
Q15.41
median5.464
Q35.512
95-th percentile5.5464
Maximum5.624
Range0.392
Interquartile range (IQR)0.102

Descriptive statistics

Standard deviation0.072501289
Coefficient of variation (CV)0.013297613
Kurtosis0.17055425
Mean5.4522034
Median Absolute Deviation (MAD)0.048
Skewness-0.42583566
Sum643.36
Variance0.0052564369
MonotonicityNot monotonic
2024-10-13T17:47:32.186141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
5.512 10
 
6.6%
5.52 10
 
6.6%
5.496 8
 
5.3%
5.48 7
 
4.6%
5.36 6
 
3.9%
5.464 6
 
3.9%
5.424 6
 
3.9%
5.448 6
 
3.9%
5.472 5
 
3.3%
5.384 5
 
3.3%
Other values (23) 49
32.2%
(Missing) 34
22.4%
ValueCountFrequency (%)
5.232 1
 
0.7%
5.28 1
 
0.7%
5.296 2
 
1.3%
5.312 2
 
1.3%
5.32 1
 
0.7%
5.336 1
 
0.7%
5.352 3
2.0%
5.36 6
3.9%
5.368 2
 
1.3%
5.376 2
 
1.3%
ValueCountFrequency (%)
5.624 2
 
1.3%
5.584 2
 
1.3%
5.56 2
 
1.3%
5.544 1
 
0.7%
5.536 2
 
1.3%
5.528 2
 
1.3%
5.52 10
6.6%
5.512 10
6.6%
5.504 1
 
0.7%
5.496 8
5.3%

turb_pino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)52.5%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean32.242712
Minimum24.4
Maximum50.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:32.259046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile25.68
Q128.78
median31.16
Q334.78
95-th percentile41.064
Maximum50.64
Range26.24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2612247
Coefficient of variation (CV)0.16317562
Kurtosis2.3672383
Mean32.242712
Median Absolute Deviation (MAD)2.84
Skewness1.3329282
Sum3804.64
Variance27.680485
MonotonicityNot monotonic
2024-10-13T17:47:32.461821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.2 6
 
3.9%
30 4
 
2.6%
29.68 4
 
2.6%
34.88 3
 
2.0%
25.28 3
 
2.0%
34.08 3
 
2.0%
30.96 3
 
2.0%
46.08 2
 
1.3%
37.76 2
 
1.3%
34.48 2
 
1.3%
Other values (52) 86
56.6%
(Missing) 34
 
22.4%
ValueCountFrequency (%)
24.4 2
1.3%
25.28 3
2.0%
25.68 2
1.3%
25.92 2
1.3%
26.08 2
1.3%
26.56 2
1.3%
26.64 2
1.3%
27.12 1
 
0.7%
27.28 2
1.3%
27.52 2
1.3%
ValueCountFrequency (%)
50.64 2
1.3%
49.2 1
0.7%
46.08 2
1.3%
42.56 1
0.7%
40.8 2
1.3%
40.4 2
1.3%
39.92 2
1.3%
37.76 2
1.3%
37.68 1
0.7%
37.12 2
1.3%

f_h_inicio_pino
Date

MISSING 

Distinct61
Distinct (%)51.7%
Missing34
Missing (%)22.4%
Memory size1.3 KiB
Minimum2023-03-17 05:00:00+00:00
Maximum2024-03-20 23:30:00+00:00
2024-10-13T17:47:32.533137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:32.609094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

f_h_fin_pino
Date

MISSING 

Distinct68
Distinct (%)57.6%
Missing34
Missing (%)22.4%
Memory size1.3 KiB
Minimum2023-03-27 05:21:00+00:00
Maximum2024-03-22 06:00:00+00:00
2024-10-13T17:47:32.683931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:32.758182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dur_pino
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)29.7%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean122049.66
Minimum90000
Maximum956520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:32.827839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum90000
5-th percentile93600
Q196300
median110100
Q3111900
95-th percentile127800
Maximum956520
Range866520
Interquartile range (IQR)15600

Descriptive statistics

Standard deviation111298.85
Coefficient of variation (CV)0.91191442
Kurtosis53.786852
Mean122049.66
Median Absolute Deviation (MAD)9300
Skewness7.3295168
Sum14401860
Variance1.2387433 × 1010
MonotonicityNot monotonic
2024-10-13T17:47:32.888710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
111600 13
 
8.6%
95400 11
 
7.2%
96300 9
 
5.9%
109800 8
 
5.3%
110700 7
 
4.6%
117000 5
 
3.3%
111000 5
 
3.3%
95700 4
 
2.6%
90000 4
 
2.6%
111900 4
 
2.6%
Other values (25) 48
31.6%
(Missing) 34
22.4%
ValueCountFrequency (%)
90000 4
 
2.6%
93600 3
 
2.0%
94200 1
 
0.7%
94500 2
 
1.3%
94800 2
 
1.3%
94860 2
 
1.3%
95400 11
7.2%
95700 4
 
2.6%
96300 9
5.9%
96600 3
 
2.0%
ValueCountFrequency (%)
956520 2
 
1.3%
199500 2
 
1.3%
180900 1
 
0.7%
127800 2
 
1.3%
119700 3
2.0%
118800 2
 
1.3%
117600 2
 
1.3%
117000 5
3.3%
115200 2
 
1.3%
113400 2
 
1.3%

ph_1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)28.0%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean5.4522034
Minimum5.232
Maximum5.624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:32.949050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.232
5-th percentile5.3188
Q15.41
median5.464
Q35.512
95-th percentile5.5464
Maximum5.624
Range0.392
Interquartile range (IQR)0.102

Descriptive statistics

Standard deviation0.072501289
Coefficient of variation (CV)0.013297613
Kurtosis0.17055425
Mean5.4522034
Median Absolute Deviation (MAD)0.048
Skewness-0.42583566
Sum643.36
Variance0.0052564369
MonotonicityNot monotonic
2024-10-13T17:47:33.020121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
5.512 10
 
6.6%
5.52 10
 
6.6%
5.496 8
 
5.3%
5.48 7
 
4.6%
5.36 6
 
3.9%
5.464 6
 
3.9%
5.424 6
 
3.9%
5.448 6
 
3.9%
5.472 5
 
3.3%
5.384 5
 
3.3%
Other values (23) 49
32.2%
(Missing) 34
22.4%
ValueCountFrequency (%)
5.232 1
 
0.7%
5.28 1
 
0.7%
5.296 2
 
1.3%
5.312 2
 
1.3%
5.32 1
 
0.7%
5.336 1
 
0.7%
5.352 3
2.0%
5.36 6
3.9%
5.368 2
 
1.3%
5.376 2
 
1.3%
ValueCountFrequency (%)
5.624 2
 
1.3%
5.584 2
 
1.3%
5.56 2
 
1.3%
5.544 1
 
0.7%
5.536 2
 
1.3%
5.528 2
 
1.3%
5.52 10
6.6%
5.512 10
6.6%
5.504 1
 
0.7%
5.496 8
5.3%

turb_1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)52.5%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean32.242712
Minimum24.4
Maximum50.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:33.098350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile25.68
Q128.78
median31.16
Q334.78
95-th percentile41.064
Maximum50.64
Range26.24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2612247
Coefficient of variation (CV)0.16317562
Kurtosis2.3672383
Mean32.242712
Median Absolute Deviation (MAD)2.84
Skewness1.3329282
Sum3804.64
Variance27.680485
MonotonicityNot monotonic
2024-10-13T17:47:33.177286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.2 6
 
3.9%
30 4
 
2.6%
29.68 4
 
2.6%
34.88 3
 
2.0%
25.28 3
 
2.0%
34.08 3
 
2.0%
30.96 3
 
2.0%
46.08 2
 
1.3%
37.76 2
 
1.3%
34.48 2
 
1.3%
Other values (52) 86
56.6%
(Missing) 34
 
22.4%
ValueCountFrequency (%)
24.4 2
1.3%
25.28 3
2.0%
25.68 2
1.3%
25.92 2
1.3%
26.08 2
1.3%
26.56 2
1.3%
26.64 2
1.3%
27.12 1
 
0.7%
27.28 2
1.3%
27.52 2
1.3%
ValueCountFrequency (%)
50.64 2
1.3%
49.2 1
0.7%
46.08 2
1.3%
42.56 1
0.7%
40.8 2
1.3%
40.4 2
1.3%
39.92 2
1.3%
37.76 2
1.3%
37.68 1
0.7%
37.12 2
1.3%

ph_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)23.7%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean5.4629153
Minimum5.232
Maximum5.624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:33.239383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.232
5-th percentile5.36
Q15.408
median5.464
Q35.512
95-th percentile5.5704
Maximum5.624
Range0.392
Interquartile range (IQR)0.104

Descriptive statistics

Standard deviation0.068459306
Coefficient of variation (CV)0.012531643
Kurtosis0.44827608
Mean5.4629153
Median Absolute Deviation (MAD)0.052
Skewness-0.20234934
Sum644.624
Variance0.0046866765
MonotonicityNot monotonic
2024-10-13T17:47:33.320566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5.52 14
 
9.2%
5.464 11
 
7.2%
5.456 8
 
5.3%
5.44 7
 
4.6%
5.36 7
 
4.6%
5.408 6
 
3.9%
5.512 5
 
3.3%
5.368 5
 
3.3%
5.496 5
 
3.3%
5.504 5
 
3.3%
Other values (18) 45
29.6%
(Missing) 34
22.4%
ValueCountFrequency (%)
5.232 1
 
0.7%
5.296 1
 
0.7%
5.36 7
4.6%
5.368 5
3.3%
5.376 3
2.0%
5.384 2
 
1.3%
5.392 2
 
1.3%
5.4 4
2.6%
5.408 6
3.9%
5.424 3
2.0%
ValueCountFrequency (%)
5.624 2
 
1.3%
5.608 2
 
1.3%
5.584 2
 
1.3%
5.568 2
 
1.3%
5.552 3
 
2.0%
5.544 1
 
0.7%
5.528 2
 
1.3%
5.52 14
9.2%
5.512 5
 
3.3%
5.504 5
 
3.3%

turb_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)49.2%
Missing34
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean31.19322
Minimum24.4
Maximum49.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:47:33.394861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile25.336
Q127.92
median31.04
Q333.08
95-th percentile38.48
Maximum49.44
Range25.04
Interquartile range (IQR)5.16

Descriptive statistics

Standard deviation4.3877134
Coefficient of variation (CV)0.14066241
Kurtosis3.762094
Mean31.19322
Median Absolute Deviation (MAD)3.04
Skewness1.3567475
Sum3680.8
Variance19.252029
MonotonicityNot monotonic
2024-10-13T17:47:33.469735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.96 5
 
3.3%
32.16 4
 
2.6%
29.2 4
 
2.6%
34.4 4
 
2.6%
28 4
 
2.6%
27.92 4
 
2.6%
30.32 4
 
2.6%
32.48 4
 
2.6%
28.72 3
 
2.0%
32.96 3
 
2.0%
Other values (48) 79
52.0%
(Missing) 34
22.4%
ValueCountFrequency (%)
24.4 2
1.3%
25.04 2
1.3%
25.2 2
1.3%
25.36 1
0.7%
25.44 2
1.3%
26 1
0.7%
26.4 2
1.3%
26.48 2
1.3%
26.56 2
1.3%
26.8 1
0.7%
ValueCountFrequency (%)
49.44 2
1.3%
41.04 2
1.3%
40.4 1
0.7%
38.48 2
1.3%
37.68 2
1.3%
36 2
1.3%
35.68 2
1.3%
35.12 2
1.3%
35.04 1
0.7%
34.88 2
1.3%

Interactions

2024-10-13T17:47:28.054574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:00.918432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:05.713904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.111179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.396114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:09.663448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.876922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.154966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.365270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:14.701019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.028634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.370778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:18.572529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.888555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.068374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.308239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.482299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.531283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:25.759621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.819344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.304842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:01.418054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.047503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.537362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.697255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:09.974478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:11.187601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.467349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.784527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:14.999676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.342909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.685313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:18.887163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.205223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.426826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.552299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.728267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.783907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.006760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.179257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.333702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:01.768111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.137275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.610286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.768478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.048685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:11.375885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.541678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.860554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.074258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.417903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.759956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:18.959729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.279225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.457839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.580057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.755121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.814364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.034075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.210900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.371130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:01.987370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.197390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.655291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.805262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.089236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:11.412955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.581992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.903999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.116000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.460934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.801529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.113006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.317497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.500165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.617648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.790570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.854646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.072742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.249353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.416598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:02.191404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.255091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.694451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.842520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.130126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:11.452273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.624050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.950943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.162954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.502435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.841109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.150216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.355896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.547168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.661186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.833677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.901208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.116858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.296955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.464473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:02.406302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.321590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.739577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.887424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.176214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:11.497256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.671683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.998713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.331284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.552010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.886197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.195085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.402146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.595922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.708287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.879704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.952950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.163283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.345923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.506113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:02.726026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.384556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.781312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.926753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.218970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:11.537303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.721517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:14.042578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.373537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:16.594573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.934816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.237960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.442209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.639564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.748853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.920771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.995833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-10-13T17:47:27.869447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.999627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:05.086299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:06.997521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.264692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:09.530241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.735024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.021225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.229798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:14.562523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.888842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.116955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:18.437498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.744569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.930150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.169976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.353492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.399619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:25.621988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.690640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.918510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:29.040958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:05.257442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.033052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.308908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:09.572425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.780770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.063938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.274248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:14.606243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.934753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.163142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:18.481450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.790562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:20.979381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.214451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.393569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.439181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:25.666848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.731820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:27.962514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:29.211445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:05.429260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:07.071980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:08.353339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:09.619878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:10.829909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:12.110764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:13.321746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:14.656267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:15.983081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:17.325227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:18.529768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:19.841607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:21.024390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:22.262239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:23.440539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:24.485180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:25.713572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:26.778982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:47:28.011239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-13T17:47:33.530302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
centr_1_turb_cfcentr_2_turb_cfdur_cfdur_pinoid_bioid_centrlotelote_parental_cforden_encadenado_cfph_1ph_2ph_pinoproducto_1_cfproducto_2_cfturb_1turb_2turb_fin_cultivo_cfturb_inicio_cultivo_cfturb_pinoturbidez_diff_cfviab_final_cultivo_cfvol_ino_util_cf
centr_1_turb_cf1.0000.356-0.1990.280-0.2020.0000.393-0.0890.000-0.308-0.286-0.3080.037-0.0190.0780.1880.4070.4740.0780.280-0.004-0.027
centr_2_turb_cf0.3561.000-0.3020.008-0.1760.0170.228-0.1370.000-0.172-0.230-0.1720.1940.4280.0000.0690.5060.0890.0000.4670.141-0.073
dur_cf-0.199-0.3021.000-0.2320.1870.000-0.334-0.4290.5870.1420.1920.142-0.135-0.267-0.256-0.199-0.461-0.370-0.256-0.328-0.2010.167
dur_pino0.2800.008-0.2321.000-0.0360.0000.511NaN1.000-0.389-0.341-0.389-0.238-0.2460.3100.3600.0790.0460.3100.0510.165-0.029
id_bio-0.202-0.1760.187-0.0361.0000.220-0.178-0.2470.064-0.008-0.002-0.008-0.087-0.193-0.035-0.018-0.193-0.062-0.035-0.158-0.1170.029
id_centr0.0000.0170.0000.0000.2201.0001.0000.0000.0000.0000.0000.0000.0440.1260.0000.0000.0000.0000.0000.0000.2240.000
lote0.3930.228-0.3340.511-0.1781.0001.0000.9981.000-0.347-0.283-0.347-0.2200.0360.1780.2260.1940.1950.1780.133-0.082-0.173
lote_parental_cf-0.089-0.137-0.429NaN-0.2470.0000.9981.0000.748NaNNaNNaN-0.102-0.153NaNNaN0.436-0.530NaN0.467-0.3790.521
orden_encadenado_cf0.0000.0000.5871.0000.0640.0001.0000.7481.0001.0001.0001.0000.0000.1831.0001.0000.0000.4021.0000.0000.0000.270
ph_1-0.308-0.1720.142-0.389-0.0080.000-0.347NaN1.0001.0000.6601.000-0.016-0.037-0.389-0.454-0.252-0.112-0.389-0.228-0.1230.134
ph_2-0.286-0.2300.192-0.341-0.0020.000-0.283NaN1.0000.6601.0000.6600.0140.047-0.326-0.511-0.1580.031-0.326-0.150-0.111-0.134
ph_pino-0.308-0.1720.142-0.389-0.0080.000-0.347NaN1.0001.0000.6601.000-0.016-0.037-0.389-0.454-0.252-0.112-0.389-0.228-0.1230.134
producto_1_cf0.0370.194-0.135-0.238-0.0870.044-0.220-0.1020.000-0.0160.014-0.0161.0000.513-0.024-0.0870.4320.063-0.0240.4330.128-0.105
producto_2_cf-0.0190.428-0.267-0.246-0.1930.1260.036-0.1530.183-0.0370.047-0.0370.5131.0000.072-0.0740.4980.0660.0720.4960.079-0.227
turb_10.0780.000-0.2560.310-0.0350.0000.178NaN1.000-0.389-0.326-0.389-0.0240.0721.0000.6770.0340.0621.0000.0220.0570.163
turb_20.1880.069-0.1990.360-0.0180.0000.226NaN1.000-0.454-0.511-0.454-0.087-0.0740.6771.0000.0160.0900.677-0.0050.0550.192
turb_fin_cultivo_cf0.4070.506-0.4610.079-0.1930.0000.1940.4360.000-0.252-0.158-0.2520.4320.4980.0340.0161.0000.2190.0340.9440.286-0.294
turb_inicio_cultivo_cf0.4740.089-0.3700.046-0.0620.0000.195-0.5300.402-0.1120.031-0.1120.0630.0660.0620.0900.2191.0000.062-0.040-0.056-0.103
turb_pino0.0780.000-0.2560.310-0.0350.0000.178NaN1.000-0.389-0.326-0.389-0.0240.0721.0000.6770.0340.0621.0000.0220.0570.163
turbidez_diff_cf0.2800.467-0.3280.051-0.1580.0000.1330.4670.000-0.228-0.150-0.2280.4330.4960.022-0.0050.944-0.0400.0221.0000.276-0.236
viab_final_cultivo_cf-0.0040.141-0.2010.165-0.1170.224-0.082-0.3790.000-0.123-0.111-0.1230.1280.0790.0570.0550.286-0.0560.0570.2761.000-0.024
vol_ino_util_cf-0.027-0.0730.167-0.0290.0290.000-0.1730.5210.2700.134-0.1340.134-0.105-0.2270.1630.192-0.294-0.1030.163-0.236-0.0241.000

Missing values

2024-10-13T17:47:29.287325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-13T17:47:29.456134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-13T17:47:29.579656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loteorden_encadenado_cflote_parental_cfid_biof_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cfph_pinoturb_pinof_h_inicio_pinof_h_fin_pinodur_pinoph_1turb_1ph_2turb_2
0230191nan146152023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0082.417.2891.20184000000.017825NaNNaN1747.9206.00169200.073.92NaNNaNNaTNaTNaNNaNNaNNaNNaN
1230201nan146162023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0080.418.8091.20181600000.014246NaNNaN1676.1606.56169200.072.40NaNNaNNaTNaTNaNNaNNaNNaNNaN
2230211nan131702023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0066.416.1686.40248000000.017825NaNNaN1928.4968.08169200.070.24NaNNaNNaTNaTNaNNaNNaNNaNNaN
3230221nan146142023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0085.618.4883.20229600000.012912NaNNaN1782.8005.92169200.064.72NaNNaNNaTNaTNaNNaNNaNNaNNaN
4230231nan146152023-03-28 05:27:00+00:002023-03-30 08:00:00+00:0077.617.1274.40132800000.01782526.5620.881861.8402.96181980.057.285.49628.322023-03-26 03:00:00+00:002023-03-27 05:21:00+00:0094860.05.49628.325.50427.92
5230241nan146162023-03-28 05:24:00+00:002023-03-30 05:23:00+00:0076.016.5680.80199200000.01424624.5610.402161.1202.80172740.064.245.49628.322023-03-26 03:00:00+00:002023-03-27 05:21:00+00:0094860.05.49628.325.50427.92
6230251nan131702023-03-29 05:09:00+00:002023-03-31 05:29:00+00:0077.217.7687.20199200000.01782530.6429.362044.7204.48174000.069.445.48026.562023-03-17 05:00:00+00:002023-03-28 05:42:00+00:00956520.05.48026.565.52027.52
7230261nan146142023-03-29 05:29:00+00:002023-03-31 05:38:00+00:0078.818.2481.20206400000.01424626.4811.602263.2003.44173340.062.965.48026.562023-03-17 05:00:00+00:002023-03-28 05:42:00+00:00956520.05.48026.565.52027.52
8230271nan146152023-04-04 08:32:00+00:002023-04-06 10:30:00+00:0083.216.8868.08195200000.01424626.249.841407.6804.08179880.051.205.38433.842023-04-02 03:00:00+00:002023-04-03 11:30:00+00:00117000.05.38433.845.40032.48
9230281nan146162023-04-04 08:34:00+00:002023-04-06 10:32:00+00:0083.618.5667.20176000000.01782527.2812.161373.2004.72179880.048.645.38433.842023-04-02 03:00:00+00:002023-04-03 11:30:00+00:00117000.05.38433.845.40032.48
loteorden_encadenado_cflote_parental_cfid_biof_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cfph_pinoturb_pinof_h_inicio_pinof_h_fin_pinodur_pinoph_1turb_1ph_2turb_2
14224046224041.0131692024-03-11 12:10:00+00:002024-03-13 09:50:00+00:0080.0019.9288.00156800000.01291232.7228.321783.847.84164400.068.08NaNNaNNaTNaTNaNNaNNaNNaNNaN
143240431nan146142024-03-12 06:25:00+00:002024-03-14 07:25:00+00:0080.0019.4469.68132800000.01424630.3216.161254.564.72176400.050.245.51226.082024-03-09 23:30:00+00:002024-03-11 06:15:00+00:00110700.05.51226.085.62425.20
144240451nan146162024-03-12 06:25:00+00:002024-03-14 08:15:00+00:0080.0017.5272.48139200000.01291227.8417.761573.525.76179400.054.965.51226.082024-03-09 23:30:00+00:002024-03-11 06:15:00+00:00110700.05.51226.085.62425.20
145240441nan131702024-03-16 08:20:00+00:002024-03-18 07:01:00+00:0083.6019.2877.52160800000.01424630.7220.681528.725.44168060.058.245.42424.402024-03-13 23:30:00+00:002024-03-15 06:15:00+00:00110700.05.42424.405.40827.92
14624047224044.0131702024-03-18 12:00:00+00:002024-03-20 06:00:00+00:0080.0018.2486.40223200000.01424628.1626.761794.326.64151200.068.16NaNNaNNaTNaTNaNNaNNaNNaNNaN
147240491nan146172024-03-16 08:22:00+00:002024-03-18 07:23:00+00:0083.6018.8872.64164800000.01291230.5617.001342.804.88169260.053.765.42424.402024-03-13 23:30:00+00:002024-03-15 06:15:00+00:00110700.05.42424.405.40827.92
148240501nan146142024-03-23 07:57:00+00:002024-03-25 07:28:00+00:0084.1617.7667.60152000000.0637929.4426.641422.803.68171060.049.845.49634.002024-03-20 23:30:00+00:002024-03-22 06:00:00+00:00109800.05.49634.005.37635.12
149240511nan131692024-03-23 07:57:00+00:002024-03-25 07:33:00+00:0084.1617.7680.80160800000.01291233.4419.321486.565.52171360.063.045.49634.002024-03-20 23:30:00+00:002024-03-22 06:00:00+00:00109800.05.49634.005.37635.12
15024052224050.0146142024-03-25 12:28:00+00:002024-03-27 07:51:00+00:0086.4017.2869.04148000000.01424623.6818.201857.286.00156180.051.76NaNNaNNaTNaTNaNNaNNaNNaNNaN
15124053224051.0131692024-03-25 11:27:00+00:002024-03-27 07:27:00+00:0087.2016.7279.36148000000.01291226.5619.161784.087.20158400.062.64NaNNaNNaTNaTNaNNaNNaNNaNNaN